Accurate wireless localization is a key enabler for 6G networks, yet remains challenging under diverse and rapidly changing propagation conditions. Model-based methods degrade when multipath channels are non-resolvable and model mismatches occur, while supervised deep learning demands large labeled datasets and generalizes poorly to new deployments. Inspired by foundation models (FMs) in language and vision, this article presents a unified framework for FM-based wireless localization that learns transferable channel representations from large-scale unlabeled channel state information and adapts to new environments with minimal or even no supervision. We review the fundamentals of FMs, compare the FM paradigm with existing localization approaches, and introduce a three-stage framework spanning large-scale pretraining, localization-oriented fine-tuning, and context-augmented inference, together with the location-aware applications it enables. Ray-tracing-based case studies show improved positioning accuracy and cross-environment generalization. Finally, we present an outlook on key research directions toward AI-native networks for wireless localization.
Guangjin Pan, Jiajia Guo, Zheng Xing et al.· 0 citations
Device-free human pose estimation using commodity WiFi signals has emerged as a promising paradigm for pervasive sensing in mobile computing systems. However, existing approaches often suffer from severe performance degradation when deployed across heterogeneous environments due to complex multipath propagation and domain shifts in wireless signals. In this paper, we present a physics-informed WiFi sensing framework for robust 3D human pose estimation under mobile and cross-environment settings. Our approach explicitly models wireless signal propagation characteristics and incorporates multipath-aware attention to capture environment-dependent signal variations. To further improve generalization, we introduce a disentangled representation learning scheme that separates pose-related features from environment-specific factors, enabling effective cross-domain adaptation without requiring extensive retraining. We implement our system using commodity WiFi devices and evaluate it on multiple public benchmarks, including Person-in-WiFi-3D and MM-Fi, as well as real-world deployments across diverse indoor environments. Experimental results demonstrate that our framework significantly improves robustness and generalization performance compared to state-of-the-art methods, particularly under cross-environment scenarios. These results highlight the potential of physics-informed wireless sensing for enabling reliable, scalable, and infrastructure-free human-centric applications in mobile computing systems.
Kaixuan Huang, Yuanbo Chen, Guangjin Pan et al.· 0 citations
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